Pokemon Characteristics and Classification

Data Set:

Pokemon dataset contains information on a total of 801 Pokemon.

it includes:

• The English name of the Pokemon
• percentage_male: The percentage of the species that are male. Blank if the Pokemon is genderless.
• height_m: Height of the Pokemon in metres
• weight_kg: The Weight of the Pokemon in kilograms
• hp: The Base HP of the Pokemon
• attack: The Base Attack of the Pokemon
• defense: The Base Defense of the Pokemon
• speed: The Base Speed of the Pokemon
• base_egg_steps: The number of steps required to hatch an egg of the Pokemon
• base_happiness: Base Happiness of the Pokemon
• capture_rate: Capture Rate of the Pokemon
• experience_growth: The Experience Growth of the Pokemon
• sp_attack: The Base Special Attack of the Pokemon
• sp_defense: The Base Special Defense of the Pokemon
• generation: The numbered generation which the Pokemon was first introduced
• is_legendary: Denotes if the Pokemon is legendary(two major classes: legendary pokemons and not legendary pokemons)

Data are downloaded from: https://www.kaggle.com/rounakbanik/pokemon/data

Packages needed:

library('DT')
library('missMDA')
library('NbClust')
library("FactoMineR")
library('factoextra')
library('fossil')
library("corrplot")
library('plotly')

D=read.csv('Pokemon.csv',header = T,row.names = 1)
datatable(D, rownames = 1, filter="top", options = list(pageLength = 5, scrollX=T) )

summary(D)

##         names     percentage_male     height_m        weight_kg     
##  Abomasnow :  1   Min.   :  0.00   Min.   : 0.100   Min.   :  0.10  
##  Abra      :  1   1st Qu.: 50.00   1st Qu.: 0.600   1st Qu.:  9.00  
##  Absol     :  1   Median : 50.00   Median : 1.000   Median : 27.30  
##  Accelgor  :  1   Mean   : 55.16   Mean   : 1.164   Mean   : 61.38  
##  Aegislash :  1   3rd Qu.: 50.00   3rd Qu.: 1.500   3rd Qu.: 64.80  
##  Aerodactyl:  1   Max.   :100.00   Max.   :14.500   Max.   :999.90  
##  (Other)   :795   NA's   :98       NA's   :20       NA's   :20      
##        hp             attack          defense           speed       
##  Min.   :  1.00   Min.   :  5.00   Min.   :  5.00   Min.   :  5.00  
##  1st Qu.: 50.00   1st Qu.: 55.00   1st Qu.: 50.00   1st Qu.: 45.00  
##  Median : 65.00   Median : 75.00   Median : 70.00   Median : 65.00  
##  Mean   : 68.96   Mean   : 77.86   Mean   : 73.01   Mean   : 66.33  
##  3rd Qu.: 80.00   3rd Qu.:100.00   3rd Qu.: 90.00   3rd Qu.: 85.00  
##  Max.   :255.00   Max.   :185.00   Max.   :230.00   Max.   :180.00  
##                                                                     
##  base_egg_steps  base_happiness    capture_rate    experience_growth
##  Min.   : 1280   Min.   :  0.00   Min.   :  3.00   Min.   : 600000  
##  1st Qu.: 5120   1st Qu.: 70.00   1st Qu.: 45.00   1st Qu.:1000000  
##  Median : 5120   Median : 70.00   Median : 60.00   Median :1000000  
##  Mean   : 7191   Mean   : 65.36   Mean   : 98.76   Mean   :1054996  
##  3rd Qu.: 6400   3rd Qu.: 70.00   3rd Qu.:170.00   3rd Qu.:1059860  
##  Max.   :30720   Max.   :140.00   Max.   :255.00   Max.   :1640000  
##                                   NA's   :1                         
##    sp_attack        sp_defense       generation    is_legendary    
##  Min.   : 10.00   Min.   : 20.00   Min.   :1.00   Min.   :0.00000  
##  1st Qu.: 45.00   1st Qu.: 50.00   1st Qu.:2.00   1st Qu.:0.00000  
##  Median : 65.00   Median : 66.00   Median :4.00   Median :0.00000  
##  Mean   : 71.31   Mean   : 70.91   Mean   :3.69   Mean   :0.08739  
##  3rd Qu.: 91.00   3rd Qu.: 90.00   3rd Qu.:5.00   3rd Qu.:0.00000  
##  Max.   :194.00   Max.   :230.00   Max.   :7.00   Max.   :1.00000  
## 

PCA on Characteristics of Pokemons:

Variables from height_m to sp_defense are the quantitatives variables for the PCA.

Supplementary qualitative variable are generation and ‘is_legendary’.

impData=imputePCA(D[,3:14], ncp = 2, scale = TRUE, method = c("Regularized","EM")) # Impute missing data
ImputData=data.frame(cbind(impData[["completeObs"]],D[,15]))
res.pca = PCA(ImputData[,1:13], graph = FALSE,quali.sup = 13)

head(res.pca$eig,4)

##        eigenvalue percentage of variance cumulative percentage of variance
## comp 1  4.6406574              38.672145                          38.67214
## comp 2  1.3770799              11.475666                          50.14781
## comp 3  1.2355183              10.295986                          60.44380
## comp 4  0.8768248               7.306873                          67.75067

fviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))

In our case, we are studying the 3 first dimensions (60.44% variance cumulative percentage with an eigenvalue >1)

Visualizing the first 2 dimensions:

corrplot(res.pca$var$cos2, is.corr=FALSE)

fviz_pca_var(res.pca, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),repel = T
             )

Optimal number of clusters:

NbCLust will be used to determine the optimal number of clusters for the k means and for hierarchical clustering, bellow is the code:

X=scale(ImputData[,1:12]) # Contains scaled charasteristics of each pokemon

res_nbclust<-NbClust(X,min.nc = 2, max.nc = 20, index="silhouette",method = "kmeans")
res_nbclust$All.index

##      2      3      4      5      6      7      8      9     10     11 
## 0.2238 0.2342 0.2334 0.2011 0.1504 0.1480 0.1147 0.1238 0.1206 0.1339 
##     12     13     14     15     16     17     18     19     20 
## 0.1273 0.1380 0.1335 0.1258 0.1206 0.1282 0.1303 0.1269 0.1289

In the following, 3 is considered the optimal cluster’s number with a silhouette index equal to 0.2342.

Clustering Using kmeans:

km=kmeans(X,3)
d=data.frame(km$centers)
d

##     height_m   weight_kg         hp     attack    defense      speed
## 1  1.6998535  1.88177010  1.1646459  1.1354725  1.0251661  0.8160740
## 2  0.1316936  0.01270379  0.3344813  0.3523130  0.3339804  0.2742898
## 3 -0.5410006 -0.41989423 -0.6991946 -0.7169343 -0.6686642 -0.5436197
##   base_egg_steps base_happiness capture_rate experience_growth  sp_attack
## 1      2.6817483     -1.9642062   -0.9950961        1.20658511  1.2453287
## 2     -0.1924714      0.1936187   -0.5191537       -0.05644976  0.3248104
## 3     -0.3151650      0.1600267    0.9112943       -0.18235096 -0.7034575
##   sp_defense
## 1  1.1521201
## 2  0.3887621
## 3 -0.7695236

plot_ly(
  type = 'scatterpolar',
  fill = 'toself',
  mode='markers'
) %>%
  add_trace(
    r = as.numeric(d[1,]),
    theta = colnames(d),
    name = 'Group A'
  )%>%
  add_trace(
    r = as.numeric(d[2,]),
    theta = colnames(d),
    name = 'Group B'
  )%>%
  add_trace(
    r = as.numeric(d[3,]),
    theta = colnames(d),
    name = 'Group D'
  )%>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(-3,3)
      )
    )
  )

fviz_cluster(list(data = X, cluster = km$cluster), geom = "point", stand = FALSE, palette = "jco")

d=dist(X,method = 'euclidean') # distance= "aitchison", "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"

hc1=hclust(d,method = 'ward.D') #method ="single", "complete", "average", "mcquitty", "ward.D", "ward.D2", "centroid" or "median"